Health Monitoring and Remaining Useful Life

Estimation of Lithium-Ion Aeronautical Batteries

Dr. Cairo Lúcio Nascimento JúniorEng. Prof.M. José Affonso Moreira PennaEng. M.Sc. Leonardo Ramos Rodrigues

Instituto Tecnológico de Aeronáutica

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Introduction

PHM (Prognostics and Health Management)

Lithium-Ion Battery

Capacity Model

Health Monitoring Model

Model Simulations

Estimating Remaining Useful Life

Case Study

Conclusions

Summary

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•Study of lithium-ion’s battery•Analyses of

experimental data

Models

•Simulation of battery discharges

through the life cycle

Monitoring • Study and application of techniques

for PHM

RUL Estimation

Motivation Reducing costs of operation and maintenance;

Flight safety improvement;

Development of techniques for prognosis.

Objective Develop methodology for estimating the Remaining Useful Life (RUL) of

lithium-ion’s aeronautical battery.

Methodology

Introduction

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Present scenario:

◦ MTBF (Mean Time Between Failures); Maintenance tasks are assigned based on hard times; Decrease in the dispatch of the aircraft; Insufficient data to predict failure; Possible degradation in flight safety;

Current proposals :

◦ Data-Driven Methods;◦ Model-Based Methods.

PHM (Prognostics and Health Management)

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Fundamental concepts:

1. All electromechanical systems age as a function of use, passage of time, and environmental conditions;

2. Component aging and damage accumulation is a monotonic process that manifests itself in the physical and chemical composition of the component;

3. Signs of aging (either direct or indirect) are detectable prior to overt failure of the component (i.e., loss of function);

4. It is possible to correlate signs of aging with a model of component aging and thereby estimate remaining useful life of individual components.

PHM (Prognostics and Health Management)

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Data-driven methods

◦ Capture and analyze multi-dimensional and noisy data containing a large number of variables related to component degradation;

◦ Management of uncertainty;

PHM (Prognostics and Health Management)

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Model-based methods

◦ Development of first-principles models of component use and damage accumulation;

◦ Use operational data to fine-tune model parameters;

◦ Model-based prognostics typically result in more accurate and precise RUL estimation;

◦ Advantages in validation, verification, and certification since the model response can be correlated with laws of nature.

PHM (Prognostics and Health Management)

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Why study this type of battery?

◦ Increasing application in the aerospace industry (Boeing 787, Airbus A380);

◦ Higher energy density, low self-discharge, long life in stock;◦ Available experimental data at NASA Ames Prognostics Data Repository.

Lithium-Ion Batteries

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Failure modes

◦ Over-voltage;◦ Under-voltage;◦ Low temperature operation;◦ High temperature operation; ◦ Mechanical fatigue; Life Cycle

Lithium-Ion Batteries

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Data Repository

Source: NASA Ames Prognostics Data Repository; 34 lithium-ion batteries (Cnominal=2Ah); Repetitive cycles of discharge, recharge, and impedance

measurement; Archives ”.mat”.

Discharge and Capacity Model

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◦ Data treatment Extrapolation of discharge curve.

◦ Effect of degradation over the life cycle Reduced time of discharge; Reduction of voltage.

Discharge and Capacity Model

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Data repository

◦ Battery capacity (C) calculated by

◦ State of Charge (SoC) calculated by

Discharge and Capacity Model

finalt

dttIC0

)(

1

0

)(1

)()( 01

t

t

dttIC

tSoCtSoC

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Discharge model

◦ (PAATERO, 1997) e (SPERANDIO, 2010);

◦ Voltage U (I,T,SoC) calculated by

Discharge and Capacity Model

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Discharge model

◦ Determination of parameters x1...x17:

First discharge curve of each selected batteries; FMINSEARCH (MATLAB®) minimizing square

error; Error mean=0,0565 V (<1.8%); Error variance= 0,0058 V2.

Discharge and Capacity Model

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Capacity model

◦ Linear model Capacity = f (T, I, nc)

◦ Determination of the parameters c0 and c1:

Selected five batteries with different discharge profiles; Selected c0 and c1 models; fminsearch (MATLAB®) minimizing square error; Error mean=0,0324 Ah (<2,2%); Error variance=0,0035 (Ah)2

Discharge and Capacity Model

),(),(),,( 01 TIcncTIcncTIC

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Capacity model

◦ Capacity x electrical current

Low electrical current:

Higher initial capacity C0; Faster loss of capacity.

◦ Capacity x temperature

High temperature:

Higher initial capacity C0; Faster loss of capacity.

Discharge and Capacity Model

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◦ State of Health (SoH)

◦ Delta Health

◦ nc(C=0)

◦ Capacidade @SoH

◦ Relative Number of Cycles (ncr)

◦ Remaining Useful Life (RUL)

Health Monitoring Model

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◦ Battery Model

Capacity Model

SoC calc

Discharge Model

Model Simulations

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◦ Health Monitoring System

Source

Model Simulations

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◦ Health Monitoring Model

SoH calc

Model Simulations

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rnc calc

SoH calc

Model Simulations

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◦ Example of Simulation

Example of the evaluation of SoH, delta health and nrc at determinate operation profile throughout the life of the battery. At the cycle 210 the discharge profile change from I=4A and T=43°C to I=2A and T=24°C.

Model Simulations

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Estimating Remaining Useful Life

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Method proposed to estimate the remaining useful life (RULmin and RULmax):

1. A linear regression of the SoH data available to date using the function REGRESS (Matlab R2010b);

2. Evaluation of the cycle number at which the battery reaches the minimum threshold of SoH (SoHmin) by extrapolating the line obtained by linear regression;

3. Addition of the uncertainty of the model and of the future operating profile to be performed.

Estimating Remaining Useful Life

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1. A linear regression:

2. Evaluation of ncfailure and RUL::

3. Addition of the uncertainty:

Case Study

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◦ Case A

electrical starting of the engines; 15 minutes discharge; I=4A (exponential decay); T=43ºC.

Simulation failure at cycle 495

Even if the battery can execute the starting profile until cycle number 770, the battery cannot comply with the emergency requirement after cycle 495, as shown in Figure 20. In this case the failure of the battery is declared on cycle 495.

Case Study

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◦ Case A

RUL estimation good accuracy; good precision (approximately 34 cycles)

Case Study

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◦ Case B

Nominal operation of I=4A and T=24ºC; Non-anticipated degradation; Increase on the ambient temperature

(T=43ºC) during 25 cycles.

Simulation failure antecipated from cycle 1130 to cycle 1059

Case Study

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◦ Case B

RUL estimation good response and accuracy even with a

dynamical change; good precision (approximately 79 cycles).

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Contact:

Prof. Dr. Cairo Lúcio Nascimento Júniorcairo@ita.com

Eng. M.Sc. José Affonso Moreira Pennazeaffonso@gmail.com

Eng. M.Sc. Leonardo Ramos Rodriguesleonardo.ramos@embraer.com.br

Instituto Tecnológico

de Aeronáutica